OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Areas of Potential High Prevalence of Chagas in Argentina
Venue: arXiv cs.SI 2020
Year: 2020

A map of potential prevalence of Chagas disease (ChD) with high spatial disaggregation is presented. It aims to detect areas outside the Gran Chaco ecoregion (hyperendemic for the ChD), characterized by high affinity with ChD and high health vulnerability. To quantify potential prevalence, we developed several indicators: an Affinity Index which quantifies the degree of linkage between endemic areas of ChD and the rest of the country. We also studied favorable habitability conditions for Triatoma infestans, looking for areas where the predominant materials of floors, roofs and internal ceilings favor the presence of the disease vector. We studied determinants of a more general nature that can be encompassed under the concept of Health Vulnerability Index. These determinants are associated with access to health providers and the socio-economic level of different segments of the population. Finally we constructed a Chagas Potential Prevalence Index (ChPPI) which combines the affinity index, the health vulnerability index, and the population density. We show and discuss the maps obtained. These maps are intended to assist public health specialists, decision makers of public health policies and public officials in the development of cost-effective strategies to improve access to diagnosis and treatment of ChD.
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Venue: arXiv cs.CL 2020
Year: 2020

Text summarization aims to extract essential information from a piece of text and transform the text into a concise version. Existing unsupervised abstractive summarization models leverage recurrent neural networks framework while the recently proposed transformer exhibits much more capability. Moreover, most of previous summarization models ignore abundant unlabeled corpora resources available for pretraining. In order to address these issues, we propose TED, a transformer-based unsupervised abstractive summarization system with pretraining on large-scale data. We first leverage the lead bias in news articles to pretrain the model on millions of unlabeled corpora. Next, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of generated summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are highly abstractive, and each component in the objective function of TED is highly effective.
Stability and Memory-loss go Hand-in-Hand: Three Results in Dynamics & Computation
Venue: arXiv cs.LG 2020
Year: 2020

The search for universal laws that help establish a relationship between dynamics and computation is driven by recent expansionist initiatives in biologically inspired computing. A general setting to understand both such dynamics and computation is a driven dynamical system that responds to a temporal input. Surprisingly, we find memory-loss a feature of driven systems to forget their internal states helps provide unambiguous answers to the following fundamental stability questions that have been unanswered for decades: what is necessary and sufficient so that slightly different inputs still lead to mostly similar responses? How does changing the driven system's parameters affect stability? What is the mathematical definition of the edge-of-criticality? We anticipate our results to be timely in understanding and designing biologically inspired computers that are entering an era of dedicated hardware implementations for neuromorphic computing and state-of-the-art reservoir computing applications.
CryptoExplorer: An Interactive Web Platform Supporting Secure Use of Cryptography APIs
Venue: arXiv cs.SE 2020
Year: 2020

Research has shown that cryptographic APIs are hard to use. Consequently, developers resort to using code examples available in online information sources that are often not secure. We have developed a web platform, named CryptoExplorer, stocked with numerous real-world secure and insecure examples that developers can explore to learn how to use cryptographic APIs properly. This platform currently provides 3,263 secure uses, and 5,897 insecure uses of Java Cryptography Architecture mined from 2,324 Java projects on GitHub. A preliminary study shows that CryptoExplorer provides developers with secure crypto API use examples instantly, developers can save time compared to searching on the internet for such examples, and they learn to avoid using certain algorithms in APIs by studying misused API examples. We have a pipeline to regularly mine more projects, and, on request, we offer our dataset to researchers.
Characterizing Reading Time on Enterprise Emails
Venue: arXiv cs.IR 2020
Year: 2020

Email is an integral part of people's work and life, enabling them to perform activities such as communicating, searching, managing tasks and storing information. Modern email clients take a step forward and help improve users' productivity by automatically creating reminders, tasks or responses. The act of reading is arguably the only activity that is in common in most -- if not all -- of the interactions that users have with their emails. In this paper, we characterize how users read their enterprise emails, and reveal the various contextual factors that impact reading time. Our approach starts with a reading time analysis based on the reading events from a major email platform, followed by a user study to provide explanations for some discoveries. We identify multiple temporal and user contextual factors that are correlated with reading time. For instance, email reading time is correlated with user devices: on desktop reading time increases through the morning and peaks at noon but on mobile it increases through the evening till midnight. The reading time is also negatively correlated with the screen size. We have established the connection between user status and reading time: users spend more time reading emails when they have fewer meetings and busy hours during the day. In addition, we find that users also reread emails across devices. Among the cross-device reading events, 76% of reread emails are first visited on mobile and then on desktop. Overall, our study is the first to characterize enterprise email reading time on a very large scale. The findings provide insights to develop better metrics and user models for understanding and improving email interactions.
Multi-Gradient Descent for Multi-Objective Recommender Systems
Venue: arXiv cs.IR 2020
Year: 2020

Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multiple stakeholders - sellers, buyers, shareholders - in addition to legal and ethical constraints. Simultaneously optimizing for a multitude of objectives, correlated and not correlated, having the same scale or not, has proven difficult so far. We introduce a stochastic multi-gradient descent approach to recommender systems (MGDRec) to solve this problem. We show that this exceeds state-of-the-art methods in traditional objective mixtures, like revenue and recall. Not only that, but through gradient normalization we can combine fundamentally different objectives, having diverse scales, into a single coherent framework. We show that uncorrelated objectives, like the proportion of quality products, can be improved alongside accuracy. Through the use of stochasticity, we avoid the pitfalls of calculating full gradients and provide a clear setting for its applicability.
TrappeD: DRAM Trojan Designs for Information Leakage and Fault Injection Attacks
Venue: arXiv cs.AR 2020
Year: 2020

In this paper, we investigate the advanced circuit features such as wordline- (WL) underdrive (prevents retention failure) and overdrive (assists write) employed in the peripherals of Dynamic RAM (DRAM) memories from a security perspective. In an ideal environment, these features ensure fast and reliable read and write operations. However, an adversary can re-purpose them by inserting Trojans to deliver malicious payloads such as fault injections, Denial-of-Service (DoS), and information leakage attacks when activated by the adversary. Simulation results indicate that wordline voltage can be increased to cause retention failure and thereby launch a DoS attack in DRAM memory. Furthermore, two wordlines or bitlines can be shorted to leak information or inject faults by exploiting the DRAM's refresh operation. We demonstrate an information leakage system exploit by implementing TrappeD on RocketChip SoC.
On the Finite Optimal Convergence of Logic-Based Benders' Decomposition in Solving 0-1 Min-max Regret Optimization Problems with Interval Costs
Venue: arXiv cs.DS 2020
Year: 2020

This paper addresses a class of problems under interval data uncertainty composed of min-max regret versions of classical 0-1 optimization problems with interval costs. We refer to them as interval 0-1 min-max regret problems. The state-of-the-art exact algorithms for this class of problems work by solving a corresponding mixed integer linear programming formulation in a Benders' decomposition fashion. Each of the possibly exponentially many Benders' cuts is separated on the fly through the resolution of an instance of the classical 0-1 optimization problem counterpart. Since these separation subproblems may be NP-hard, not all of them can be modeled by means of linear programming, unless P = NP. In these cases, the convergence of the aforementioned algorithms are not guaranteed in a straightforward manner. In fact, to the best of our knowledge, their finite convergence has not been explicitly proved for any interval 0-1 min-max regret problem. In this work, we formally describe these algorithms through the definition of a logic-based Benders' decomposition framework and prove their convergence to an optimal solution in a finite number of iterations. As this framework is applicable to any interval 0-1 min-max regret problem, its finite optimal convergence also holds in the cases where the separation subproblems are NP-hard.
Privacy in Data Service Composition
Venue: arXiv cs.DB 2020
Year: 2020

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data
Venue: arXiv cs.LG 2020
Year: 2020

Many real-world phenomena are observed at multiple resolutions. Predictive models designed to predict these phenomena typically consider different resolutions separately. This approach might be limiting in applications where predictions are desired at fine resolutions but available training data is scarce. In this paper, we propose classification algorithms that leverage supervision from coarser resolutions to help train models on finer resolutions. The different resolutions are modeled as different views of the data in a multi-view framework that exploits the complementarity of features across different views to improve models on both views. Unlike traditional multi-view learning problems, the key challenge in our case is that there is no one-to-one correspondence between instances across different views in our case, which requires explicit modeling of the correspondence of instances across resolutions. We propose to use the features of instances at different resolutions to learn the correspondence between instances across resolutions using an attention mechanism.Experiments on the real-world application of mapping urban areas using satellite observations and sentiment classification on text data show the effectiveness of the proposed methods.
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Venue: arXiv cs.RO 2020
Year: 2020

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.
Measuring the Diversity of Facebook Reactions to Research
Venue: arXiv cs.SI 2020
Year: 2020

Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared---a conclusion that contradicts previous studies of other social media platforms.
A Comprehensive Survey of Multilingual Neural Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.
Search techniques in peer to peer networks
Venue: arXiv cs.NI 2020
Year: 2020

Peer to peer (P2P) networks are an overlay on IP network of the internet and they can shape the future of computing by their involvement in distributed systems with the increased of use of low priced personal computers to form big clusters of distributed systems. An important problem for P2P networks and models is searching for data in the network which can be the basis of any service that uses such a network. Here we explore the major types of P2P networks and their solution to such a problem and we explore improvements that happened to these networks to be more appealing for commercial usage by offering features of load balancing, scalability, self organization and fault tolerance.
New lower bounds for Massively Parallel Computation from query complexity
Venue: arXiv cs.DS 2020
Year: 2020

Roughgarden, Vassilvitskii, and Wang (JACM 18) recently introduced a novel framework for proving lower bounds for Massively Parallel Computation using techniques from boolean function complexity. We extend their framework in two different ways, to capture two common features of Massively Parallel Computation: $\circ$ Adaptivity, where machines can write to and adaptively read from shared memory throughout the execution of the computation. Recent work of Behnezhad et al. (SPAA 19) showed that adaptivity enables significantly improved round complexities for a number of central graph problems. $\circ$ Promise problems, where the algorithm only has to succeed on certain inputs. These inputs may have special structure that is of particular interest, or they may be representative of hard instances of the overall problem. Using this extended framework, we give the first unconditional lower bounds on the complexity of distinguishing whether an input graph is a cycle of length $n$ or two cycles of length $n/2$. This promise problem, 1v2-Cycle, has emerged as a central problem in the study of Massively Parallel Computation. We prove that any adaptive algorithm for the 1v2-Cycle problem with I/O capacity $O(n^{\varepsilon})$ per machine requires $Ω(1/\varepsilon)$ rounds, matching a recent upper bound of Behnezhad et al. In addition to strengthening the connections between Massively Parallel Computation and boolean function complexity, we also develop new machinery to reason about the latter. At the heart of our proofs are optimal lower bounds on the query complexity and approximate certificate complexity of the 1v2-Cycle problem.
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation
Venue: arXiv cs.CV 2020
Year: 2020

Video super-resolution aims at generating a high-resolution video from its low-resolution counterpart. With the rapid rise of deep learning, many recently proposed video super-resolution methods use convolutional neural networks in conjunction with explicit motion compensation to capitalize on statistical dependencies within and across low-resolution frames. Two common issues of such methods are noteworthy. Firstly, the quality of the final reconstructed HR video is often very sensitive to the accuracy of motion estimation. Secondly, the warp grid needed for motion compensation, which is specified by the two flow maps delineating pixel displacements in horizontal and vertical directions, tends to introduce additional errors and jeopardize the temporal consistency across video frames. To address these issues, we propose a novel dynamic local filter network to perform implicit motion estimation and compensation by employing, via locally connected layers, sample-specific and position-specific dynamic local filters that are tailored to the target pixels. We also propose a global refinement network based on ResBlock and autoencoder structures to exploit non-local correlations and enhance the spatial consistency of super-resolved frames. The experimental results demonstrate that the proposed method outperforms the state-of-the-art, and validate its strength in terms of local transformation handling, temporal consistency as well as edge sharpness.
Unleashing the Potentials of Immersive Augmented Reality for Software Engineering
Venue: arXiv cs.SE 2020
Year: 2020

In immersive augmented reality (IAR), users can wear a head-mounted display to see computer-generated images superimposed to their view of the world. IAR was shown to be beneficial across several domains, e.g., automotive, medicine, gaming and engineering, with positive impacts on, e.g., collaboration and communication. We think that IAR bears a great potential for software engineering but, as of yet, this research area has been neglected. In this vision paper, we elicit potentials and obstacles for the use of IAR in software engineering. We identify possible areas that can be supported with IAR technology by relating commonly discussed IAR improvements to typical software engineering tasks. We further demonstrate how innovative use of IAR technology may fundamentally improve typical activities of a software engineer through a comprehensive series of usage scenarios outlining practical application. Finally, we reflect on current limitations of IAR technology based on our scenarios and sketch research activities necessary to make our vision a reality. We consider this paper to be relevant to academia and industry alike in guiding the steps to innovative research and applications for IAR in software engineering.
Automatic Business Process Structure Discovery using Ordered Neurons LSTM: A Preliminary Study
Venue: arXiv cs.CL 2020
Year: 2020

Automatic process discovery from textual process documentations is highly desirable to reduce time and cost of Business Process Management (BPM) implementation in organizations. However, existing automatic process discovery approaches mainly focus on identifying activities out of the documentations. Deriving the structural relationships between activities, which is important in the whole process discovery scope, is still a challenge. In fact, a business process has latent semantic hierarchical structure which defines different levels of detail to reflect the complex business logic. Recent findings in neural machine learning area show that the meaningful linguistic structure can be induced by joint language modeling and structure learning. Inspired by these findings, we propose to retrieve the latent hierarchical structure present in the textual business process documents by building a neural network that leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with process-level language model objective. We tested the proposed approach on data set of Process Description Documents (PDD) from our practical Robotic Process Automation (RPA) projects. Preliminary experiments showed promising results.